Machine Learning Classifiers with Acoustic Features for Prosodic Segmentation in Brazilian Portuguese: A Comprehensive Evaluation - Craveiro, Giovana M.; Alves, Caroline A.; Svartman, Flaviane; Aluísio, Sandra M. (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2025: Anais do XVI Simpósio Brasileiro de Tecnologia da Informação e da Linguagem HumanaDO - 10.5753/stil.2025.37818, 2025)
Spontaneous speech has not yet been widely explored in Brazilian Portuguese (BP) for the task of automatic prosodic segmentation. In this article, we compared seven types of classifiers, considering their performance for various types of speaker profiles (varied genders, ages, education levels, and regions of birth) and environmental impact, and trained the most appropriate one. Thus, we propose a Random Forest classifier, based on acoustic features, with low environmental impact and an F1 score of 0.55% and 0.77%, with binary and macro averages, respectively. Additionally, we are making it publicly available and present a discussion of its efficiency for different speaker profiles, as well as its environmental impact.